FOCUS: a feature optimization and cascaded unified screening strategy for computer-aided pathology-based glioma molecular subtype classification

Abstract

Abstract Background Accurate classification of Isocitrate Dehydrogenase (IDH) mutation status is crucial for the diagnosis and treatment of adult-type diffuse gliomas as defined by the 2021 World Health Organization (WHO) Classification. However, conventional histological and molecular diagnostics are time-consuming, labor-intensive, costly, and often limited in accessibility. Recent advances in artificial intelligence have enabled automated and unbiased glioma classification directly from histopathological images, but challenges remain in capturing complex pathological and molecular features effectively. Methods In this study, we propose FOCUS (Feature Optimization and Cascaded Unified Screening), an integrated framework for IDH mutation status classification in gliomas using glioma pathology slides. The histopathological images are divided into patches, from which a Vision Transformer (ViT) extracts interpretable features related to nuclear morphology and tumor progression–associated protein expression. A Patch-based Graph Convolutional Network (PatchGCN) further aggregates spatial context to capture both local morphology and global tumor heterogeneity. A cascaded feature selection strategy is employed to refine biomarker identification, followed by a machine learning classifier for final classification. Results Using the proposed framework, the Support Vector Machine (SVM) demonstrated promising performance on a dataset of 73 patients (accuracy = 0.902, AUC = 0.909). Conclusions These results highlight the potential of the FOCUS framework as a robust and interpretable tool for IDH mutation status classification, providing a computational approach to distinguish IDH-mutant (astrocytoma and oligodendroglioma) from IDH-wildtype (glioblastoma) tumors.